Overview

Dataset statistics

Number of variables8
Number of observations2035
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory127.3 KiB
Average record size in memory64.1 B

Variable types

DateTime1
TimeSeries5
Numeric2

Timeseries statistics

Number of series5
Time series length2035
Starting point0
Ending point2034
Period1
2023-08-23T12:47:40.263138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:40.416997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Alerts

Open is highly overall correlated with High and 5 other fieldsHigh correlation
High is highly overall correlated with Open and 5 other fieldsHigh correlation
Low is highly overall correlated with Open and 5 other fieldsHigh correlation
Last is highly overall correlated with Open and 5 other fieldsHigh correlation
Close is highly overall correlated with Open and 5 other fieldsHigh correlation
Total Trade Quantity is highly overall correlated with Open and 5 other fieldsHigh correlation
Turnover (Lacs) is highly overall correlated with Open and 5 other fieldsHigh correlation
Open is non stationaryNon stationary
High is non stationaryNon stationary
Low is non stationaryNon stationary
Last is non stationaryNon stationary
Close is non stationaryNon stationary
Open is seasonalSeasonal
High is seasonalSeasonal
Low is seasonalSeasonal
Last is seasonalSeasonal
Close is seasonalSeasonal
Date has unique valuesUnique

Reproduction

Analysis started2023-08-23 10:47:33.351791
Analysis finished2023-08-23 10:47:40.201735
Duration6.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Date
Date

UNIQUE 

Distinct2035
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size16.0 KiB
Minimum2010-07-21 00:00:00
Maximum2018-09-28 00:00:00
2023-08-23T12:47:40.639961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:40.843039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Open
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct1165
Distinct (%)57.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.71373
Minimum81.1
Maximum327.7
Zeros0
Zeros (%)0.0%
Memory size16.0 KiB
2023-08-23T12:47:41.014874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81.1
5-th percentile92.22
Q1120.025
median141.5
Q3157.175
95-th percentile273.255
Maximum327.7
Range246.6
Interquartile range (IQR)37.15

Descriptive statistics

Standard deviation48.664509
Coefficient of variation (CV)0.3250504
Kurtosis2.240073
Mean149.71373
Median Absolute Deviation (MAD)18.5
Skewness1.5838736
Sum304667.45
Variance2368.2345
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3378405913
2023-08-23T12:47:41.205861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-08-23T12:47:41.709463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2023-08-23T12:47:41.803162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
148 12
 
0.6%
120 10
 
0.5%
157 9
 
0.4%
142 9
 
0.4%
155 9
 
0.4%
116 8
 
0.4%
143 8
 
0.4%
151 8
 
0.4%
110 7
 
0.3%
132 7
 
0.3%
Other values (1155) 1948
95.7%
ValueCountFrequency (%)
81.1 1
< 0.1%
82.1 1
< 0.1%
82.2 1
< 0.1%
82.25 1
< 0.1%
82.4 1
< 0.1%
82.5 1
< 0.1%
82.55 1
< 0.1%
82.6 1
< 0.1%
83.2 1
< 0.1%
83.5 1
< 0.1%
ValueCountFrequency (%)
327.7 1
< 0.1%
323 1
< 0.1%
317.75 1
< 0.1%
317.6 1
< 0.1%
315.05 2
0.1%
315 1
< 0.1%
314.65 1
< 0.1%
314.3 1
< 0.1%
314 1
< 0.1%
313.85 1
< 0.1%
2023-08-23T12:47:41.393317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACF and PACF

High
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct1257
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.99283
Minimum82.8
Maximum328.75
Zeros0
Zeros (%)0.0%
Memory size16.0 KiB
2023-08-23T12:47:42.053135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82.8
5-th percentile93.97
Q1122.1
median143.4
Q3159.4
95-th percentile276.915
Maximum328.75
Range245.95
Interquartile range (IQR)37.3

Descriptive statistics

Standard deviation49.413109
Coefficient of variation (CV)0.32510159
Kurtosis2.2121497
Mean151.99283
Median Absolute Deviation (MAD)18.8
Skewness1.5864145
Sum309305.4
Variance2441.6554
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3095187068
2023-08-23T12:47:42.244811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-08-23T12:47:42.748531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2023-08-23T12:47:42.842259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
135 14
 
0.7%
121 9
 
0.4%
119.8 8
 
0.4%
125 8
 
0.4%
154.5 7
 
0.3%
143 7
 
0.3%
148 7
 
0.3%
156.5 7
 
0.3%
147.5 6
 
0.3%
144 6
 
0.3%
Other values (1247) 1956
96.1%
ValueCountFrequency (%)
82.8 1
< 0.1%
83 1
< 0.1%
83.4 1
< 0.1%
83.45 1
< 0.1%
83.5 1
< 0.1%
83.7 1
< 0.1%
83.75 1
< 0.1%
83.85 1
< 0.1%
84.3 1
< 0.1%
84.85 1
< 0.1%
ValueCountFrequency (%)
328.75 1
< 0.1%
328.35 1
< 0.1%
326.9 1
< 0.1%
319.65 1
< 0.1%
319.2 1
< 0.1%
319.1 1
< 0.1%
318 1
< 0.1%
317.8 1
< 0.1%
317 1
< 0.1%
316.7 1
< 0.1%
2023-08-23T12:47:42.432272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACF and PACF

Low
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct1263
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.29393
Minimum80
Maximum321.65
Zeros0
Zeros (%)0.0%
Memory size16.0 KiB
2023-08-23T12:47:43.014094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile90.855
Q1118.3
median139.6
Q3155.15
95-th percentile267.885
Maximum321.65
Range241.65
Interquartile range (IQR)36.85

Descriptive statistics

Standard deviation47.931958
Coefficient of variation (CV)0.32541706
Kurtosis2.2269384
Mean147.29393
Median Absolute Deviation (MAD)18.15
Skewness1.5735418
Sum299743.15
Variance2297.4726
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.3978208064
2023-08-23T12:47:43.205556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-08-23T12:47:43.708989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2023-08-23T12:47:43.802718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
141 11
 
0.5%
115 7
 
0.3%
152.5 7
 
0.3%
119 7
 
0.3%
151 7
 
0.3%
117 6
 
0.3%
127 6
 
0.3%
112 6
 
0.3%
157.7 6
 
0.3%
116.1 5
 
0.2%
Other values (1253) 1967
96.7%
ValueCountFrequency (%)
80 1
< 0.1%
80.3 1
< 0.1%
80.5 1
< 0.1%
81 1
< 0.1%
81.1 1
< 0.1%
81.2 2
0.1%
81.3 1
< 0.1%
81.55 1
< 0.1%
82.1 1
< 0.1%
82.25 1
< 0.1%
ValueCountFrequency (%)
321.65 1
< 0.1%
315 1
< 0.1%
314 1
< 0.1%
312.55 1
< 0.1%
312.2 1
< 0.1%
311.8 2
0.1%
311.45 1
< 0.1%
310.35 1
< 0.1%
310.15 1
< 0.1%
307.55 2
0.1%
2023-08-23T12:47:43.392984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACF and PACF

Last
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct1268
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.47425
Minimum81
Maximum325.95
Zeros0
Zeros (%)0.0%
Memory size16.0 KiB
2023-08-23T12:47:43.974553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile92
Q1120.075
median141.1
Q3156.925
95-th percentile273.255
Maximum325.95
Range244.95
Interquartile range (IQR)36.85

Descriptive statistics

Standard deviation48.73257
Coefficient of variation (CV)0.32602652
Kurtosis2.2287442
Mean149.47425
Median Absolute Deviation (MAD)18.45
Skewness1.5848137
Sum304180.1
Variance2374.8634
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.412917482
2023-08-23T12:47:44.165354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-08-23T12:47:44.668020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2023-08-23T12:47:44.839855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
154.2 7
 
0.3%
115.8 6
 
0.3%
141.1 6
 
0.3%
150 6
 
0.3%
148 6
 
0.3%
153 6
 
0.3%
155.5 6
 
0.3%
149 6
 
0.3%
120.75 5
 
0.2%
96 5
 
0.2%
Other values (1258) 1976
97.1%
ValueCountFrequency (%)
81 1
< 0.1%
81.1 1
< 0.1%
81.6 1
< 0.1%
82.2 1
< 0.1%
82.4 1
< 0.1%
82.6 1
< 0.1%
82.75 1
< 0.1%
83.05 1
< 0.1%
83.4 1
< 0.1%
83.45 1
< 0.1%
ValueCountFrequency (%)
325.95 1
< 0.1%
323.7 1
< 0.1%
317.6 1
< 0.1%
317.45 1
< 0.1%
316.05 1
< 0.1%
314.7 1
< 0.1%
314.25 1
< 0.1%
314.05 1
< 0.1%
313.45 1
< 0.1%
313.1 1
< 0.1%
2023-08-23T12:47:44.352811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACF and PACF

Close
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct1313
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.45027
Minimum80.95
Maximum325.75
Zeros0
Zeros (%)0.0%
Memory size16.0 KiB
2023-08-23T12:47:45.011694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80.95
5-th percentile92.15
Q1120.05
median141.25
Q3156.9
95-th percentile273.225
Maximum325.75
Range244.8
Interquartile range (IQR)36.85

Descriptive statistics

Standard deviation48.71204
Coefficient of variation (CV)0.32594147
Kurtosis2.2257142
Mean149.45027
Median Absolute Deviation (MAD)18.45
Skewness1.5832241
Sum304131.3
Variance2372.8629
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.4115963869
2023-08-23T12:47:45.202666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2023-08-23T12:47:45.706167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Gap statistics

number of gaps0
minnan
maxnan
meannan
std0
2023-08-23T12:47:45.815545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
155.55 6
 
0.3%
154.85 6
 
0.3%
141.9 6
 
0.3%
147.65 5
 
0.2%
140.8 5
 
0.2%
153.1 5
 
0.2%
147.5 5
 
0.2%
150.2 5
 
0.2%
132.6 5
 
0.2%
145.15 5
 
0.2%
Other values (1303) 1982
97.4%
ValueCountFrequency (%)
80.95 1
< 0.1%
81.1 1
< 0.1%
81.55 1
< 0.1%
82 1
< 0.1%
82.4 1
< 0.1%
82.5 1
< 0.1%
82.55 1
< 0.1%
82.6 1
< 0.1%
82.7 1
< 0.1%
82.9 1
< 0.1%
ValueCountFrequency (%)
325.75 1
< 0.1%
323 1
< 0.1%
317.6 1
< 0.1%
316.4 2
0.1%
315.3 1
< 0.1%
314.1 1
< 0.1%
313.55 1
< 0.1%
313.3 1
< 0.1%
312.95 1
< 0.1%
312.9 1
< 0.1%
2023-08-23T12:47:45.390093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACF and PACF

Total Trade Quantity
Real number (ℝ)

HIGH CORRELATION 

Distinct2034
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2335681.4
Minimum39610
Maximum29191015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2023-08-23T12:47:46.003001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39610
5-th percentile555214.1
Q11146444.5
median1783456
Q32813594
95-th percentile6312189.1
Maximum29191015
Range29151405
Interquartile range (IQR)1667149.5

Descriptive statistics

Standard deviation2091778.1
Coefficient of variation (CV)0.89557511
Kurtosis24.055902
Mean2335681.4
Median Absolute Deviation (MAD)739863
Skewness3.644369
Sum4.7531117 × 109
Variance4.3755357 × 1012
MonotonicityNot monotonic
2023-08-23T12:47:46.190458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
839802 2
 
0.1%
3069914 1
 
< 0.1%
1762110 1
 
< 0.1%
3195668 1
 
< 0.1%
2640664 1
 
< 0.1%
2807473 1
 
< 0.1%
1442967 1
 
< 0.1%
3225051 1
 
< 0.1%
1828953 1
 
< 0.1%
2963777 1
 
< 0.1%
Other values (2024) 2024
99.5%
ValueCountFrequency (%)
39610 1
< 0.1%
100180 1
< 0.1%
108287 1
< 0.1%
111115 1
< 0.1%
125170 1
< 0.1%
163605 1
< 0.1%
169762 1
< 0.1%
193726 1
< 0.1%
207063 1
< 0.1%
210300 1
< 0.1%
ValueCountFrequency (%)
29191015 1
< 0.1%
17917625 1
< 0.1%
17805319 1
< 0.1%
17064363 1
< 0.1%
16822847 1
< 0.1%
15847309 1
< 0.1%
15744338 1
< 0.1%
14490021 1
< 0.1%
14484043 1
< 0.1%
13953152 1
< 0.1%

Turnover (Lacs)
Real number (ℝ)

HIGH CORRELATION 

Distinct2030
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3899.9806
Minimum37.04
Maximum55755.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.0 KiB
2023-08-23T12:47:46.381678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.04
5-th percentile595.585
Q11427.46
median2512.03
Q34539.015
95-th percentile12101.592
Maximum55755.08
Range55718.04
Interquartile range (IQR)3111.555

Descriptive statistics

Standard deviation4570.7679
Coefficient of variation (CV)1.1719976
Kurtosis26.067865
Mean3899.9806
Median Absolute Deviation (MAD)1332.16
Skewness4.0582341
Sum7936460.4
Variance20891919
MonotonicityNot monotonic
2023-08-23T12:47:46.553513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2140.22 2
 
0.1%
2195.84 2
 
0.1%
637.58 2
 
0.1%
1047.42 2
 
0.1%
1646.87 2
 
0.1%
4082.77 1
 
< 0.1%
2015.28 1
 
< 0.1%
2261.37 1
 
< 0.1%
2400.63 1
 
< 0.1%
3732.17 1
 
< 0.1%
Other values (2020) 2020
99.3%
ValueCountFrequency (%)
37.04 1
< 0.1%
128.04 1
< 0.1%
129.09 1
< 0.1%
147.9 1
< 0.1%
163.11 1
< 0.1%
179.78 1
< 0.1%
186.62 1
< 0.1%
200.97 1
< 0.1%
206.87 1
< 0.1%
216.49 1
< 0.1%
ValueCountFrequency (%)
55755.08 1
< 0.1%
46643.39 1
< 0.1%
44321.86 1
< 0.1%
43913.37 1
< 0.1%
37901.49 1
< 0.1%
36047.39 1
< 0.1%
33327.37 1
< 0.1%
32325.56 1
< 0.1%
32227.87 1
< 0.1%
31569.51 1
< 0.1%

Interactions

2023-08-23T12:47:38.925205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:33.675009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:34.549123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:35.454735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:36.306769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:37.155787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:38.010440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:39.049299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:33.810016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:34.667812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:35.572808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:36.423399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:37.274599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:38.138762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:39.245129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:33.930606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:34.841531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:35.690809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:36.540994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:37.391155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:38.268045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:39.359013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:34.049629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:34.959212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:35.811779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:36.657585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:37.511764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:38.397274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:39.495552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:34.167549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:35.076801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:35.927683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:36.776731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:37.630700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:38.524463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:39.619232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:34.287019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:35.195293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:36.046602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:36.894123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:37.748541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:38.654708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:39.756825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:34.422361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:35.329082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:36.178086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:37.026803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:37.883941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:47:38.792504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-23T12:47:46.694105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
OpenHighLowLastCloseTotal Trade QuantityTurnover (Lacs)
Open1.0000.9980.9970.9950.9950.5440.728
High0.9981.0000.9970.9980.9980.5650.746
Low0.9970.9971.0000.9980.9980.5340.720
Last0.9950.9980.9981.0001.0000.5540.737
Close0.9950.9980.9981.0001.0000.5540.737
Total Trade Quantity0.5440.5650.5340.5540.5541.0000.962
Turnover (Lacs)0.7280.7460.7200.7370.7370.9621.000

Missing values

2023-08-23T12:47:39.932994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-23T12:47:40.118114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateOpenHighLowLastCloseTotal Trade QuantityTurnover (Lacs)
02018-09-28234.05235.95230.20233.50233.7530699147162.35
12018-09-27234.55236.80231.10233.80233.25508285911859.95
22018-09-26240.00240.00232.50235.00234.2522409095248.60
32018-09-25233.30236.75232.00236.25236.1023493685503.90
42018-09-24233.55239.20230.75234.00233.3034235097999.55
52018-09-21235.00237.00227.95233.75234.60539531912589.59
62018-09-19235.95237.20233.45234.60234.9013620583202.78
72018-09-18237.90239.25233.50235.50235.0526147946163.70
82018-09-17233.15238.00230.25236.40236.6031708947445.41
92018-09-14223.45236.70223.30234.00233.95637790914784.50
DateOpenHighLowLastCloseTotal Trade QuantityTurnover (Lacs)
20252010-08-03118.3119.95117.90118.65118.30611234726.78
20262010-08-02117.3118.50116.60118.10117.30663593779.85
20272010-07-30116.5118.00114.60115.85116.0526561913112.68
20282010-07-29112.5121.70112.50117.50116.6023591342791.03
20292010-07-28118.0120.50117.20118.15118.25835593994.01
20302010-07-27117.6119.50112.00118.80118.65586100694.98
20312010-07-26120.1121.00117.10117.10117.60658440780.01
20322010-07-23121.8121.95120.25120.35120.65281312340.31
20332010-07-22120.3122.00120.25120.75120.90293312355.17
20342010-07-21122.1123.00121.05121.10121.55658666803.56